Combining DataFrames in Polars: The concat Method
We’ve used .join() to combine data based on a key (like a SQL JOIN). But what if you just want to stack two DataFrames on…
Time-Series in Polars: group_by_rolling for Window Analysis
We’ve covered two types of window functions in Polars. Now, we’ll look at how to use Polars group_by_rolling functionality. group_by_rolling() is the third type. It’s…
Time-Series in Polars: rolling() Window Functions
We’ve learned how to group time by month (using group_by_dynamic), but what about calculating a “7-day moving average”? This is where Polars rolling functions can…
Reshaping Data in Polars: The pivot() Method (Long to Wide)
In data analysis, you’re constantly reshaping data. we used melt() to turn “wide” data into “long” data. Today, we’re doing the opposite. pivot() is the…
A Deep Dive into the Polars Expression API (pl.Expr)
We’ve used the Polars Expression API a lot. But what is an expression? An expression, or pl.Expr, is a recipe for a calculation. It’s not…
Reshaping Data in Polars: The melt() Method (Unpivoting)
Your data is often in a “wide” format, like a spreadsheet, but analysis tools (like plotting libraries) prefer “long” format. One useful function for this…
Conditional Logic in Polars: The when/then API (SQL Case When)
One of the most common data tasks is creating a new column based on a condition. In this tutorial, we’ll focus on using Polars when…
Time-Series in Polars: group_by_dynamic Explained
One of the most common tasks in data analysis is “resampling” time data. For example, turning a list of daily sales into “Total Monthly Sales.”…
Handling Missing Data in Polars (null, fill_null, drop_nulls)
Just like Pandas has NaN, Polars has null to represent missing or empty data. Before you can analyze a dataset, you must have a strategy…
Master Polars: A Guide to groupby and Aggregations
The most common data analysis task is “Split-Apply-Combine.” When using Polars, the groupby operation is essential for this task. In Polars, this is done with…
